{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T09:09:40Z","timestamp":1778663380827,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T00:00:00Z","timestamp":1637625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871345"],"award-info":[{"award-number":["41871345"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19030101"],"award-info":[{"award-number":["XDA19030101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Oil tank inventory is significant for the economy and the military, as it can be used to estimate oil reserves. Traditional oil tank detection methods mainly focus on the geometrical characteristics and spectral features of remotely sensed images based on feature engineering. The methods have a limited application capability when the distribution pattern of ground objects in the image changes and the imaging condition varies largely. Therefore, we propose an end-to-end deep convolution network Res2-Unet+, to detect oil tanks in a large-scale area. The Res2-Unet+ method replaces the typical convolution block in the encoder of the original Unet method using hierarchical residual learning branches. A hierarchical branch is used to decompose the feature map into a few sub-channel features. To evaluate the generalization and transferability of the proposed model, we use high spatial resolution images from three different sensors in different areas to train the oil tank detection model. Images from yet another sensor in another area are used to evaluate the trained model. Three more widely used methods, Unet, Segnet, and PSPNet, are trained and evaluated for the same dataset. The experiments prove the effectiveness, strong generalization, and transferability of the proposed Res2-Unet+ method.<\/jats:p>","DOI":"10.3390\/rs13234740","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4740","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Res2-Unet+, a Practical Oil Tank Detection Network for Large-Scale High Spatial Resolution Images"],"prefix":"10.3390","volume":"13","author":[{"given":"Bo","family":"Yu","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Hainan Key Laboratory of Earth Observation, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya 572029, China"},{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"Center for Satellite Application on Ecology and Environment, Ministry of Ecology and Environment, Beijing 100006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyu","family":"Yang","sequence":"additional","affiliation":[{"name":"Jinan Environmental Monitoring Center of Shandong Province, Jinan 250013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Ma","sequence":"additional","affiliation":[{"name":"Center for Satellite Application on Ecology and Environment, Ministry of Ecology and Environment, Beijing 100006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunyan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Center for Satellite Application on Ecology and Environment, Ministry of Ecology and Environment, Beijing 100006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Center for Satellite Application on Ecology and Environment, Ministry of Ecology and Environment, Beijing 100006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2239","DOI":"10.1080\/01431161.2019.1685720","article-title":"A new approach for oil tank detection using deep learning features with control false alarm rate in high-resolution satellite imagery","volume":"41","author":"Zalpour","year":"2020","journal-title":"Int. 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